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Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors

Author

Listed:
  • Wenzhi Cao

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
    Xiangjiang Laboratory, Changsha 410205, China)

  • Houdun Liu

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

  • Xiangzhi Zhang

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

  • Yangyan Zeng

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
    Xiangjiang Laboratory, Changsha 410205, China)

  • Xiao Ling

    (State Grid Hunan Electric Power Co., Ltd. Information and Communication Branch, Changsha 410205, China)

Abstract

With the proliferation of distributed energy resources, advanced metering infrastructure, and advanced communication technologies, the grid is transforming into a flexible, intelligent, and collaborative system. Short-term electric load forecasting for individual residential customers is playing an increasingly important role in the operation and planning of the future grid. Predicting the electrical load of individual households is more challenging with higher uncertainty and volatility at the household level compared to the total electrical load at the feeder and regional levels. The previous research results show that the accuracy of forecasting using machine learning and a single deep learning model is far from adequate and there is still room for improvement.

Suggested Citation

  • Wenzhi Cao & Houdun Liu & Xiangzhi Zhang & Yangyan Zeng & Xiao Ling, 2025. "Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors," Sustainability, MDPI, vol. 17(3), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1033-:d:1578349
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    References listed on IDEAS

    as
    1. John O’Donnell & Wencong Su, 2023. "Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System," Energies, MDPI, vol. 16(15), pages 1-21, July.
    2. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    3. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    4. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    Full references (including those not matched with items on IDEAS)

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